This extract is part five of a five-part series that breaks down the layers of the Data Strategy Framework for Connected Medical Devices, which we introduced in our recent whitepaper. Featuring insights from industry leaders at Cochlear, Dexcom, Medtronic, Omron, Vocxi Health, and S3 Connected Health, the paper offers a practical framework for leveraging data to stay competitive and succeed in a data-driven healthcare landscape.
The data acquisition, architecture, and management layer form the backbone of any data strategy framework, focusing on data collection, structuring, storage, and management.
This layer is crucial for medical device vendors, particularly those involved in developing smart connected patient devices, as it ensures the reliable functioning of the devices and the secure management of the vast amounts of data they generate. It supports both the real-time and long-term data requirements of medical devices, enabling them to deliver critical services and insights to medtech organizations, healthcare providers, and patients.
"Over time, data quality can degrade, especially when devices leverage artificial intelligence, potentially leading to unsatisfactory device performance. It's vital to have robust data quality management to ensure devices continue to function effectively over time. This includes monitoring data quality continuously, as changes at the system's boundaries or integrating with other systems may affect the solution's overall effectiveness."
Aghogho Ekpruke, Research & Development, Medtronic
A critical challenge for companies developing smart connected patient devices is standardizing data management practices. The cost of managing data extends far beyond just collection — it involves ongoing service and support, storage, system management, and maintaining these processes over extended service periods. Without comprehensive planning, the costs and complexities of managing data can spiral, leading to inefficiencies and potentially undermining the success of the device. To avoid these pitfalls, companies must develop a strategic approach that accounts for the long-term needs of data management, including how data will be stored, accessed, and protected over time.
“The total cost of managing data involves more than just collection. It includes ongoing service and support, storage, data system management, and maintaining longer service periods. Comprehensive planning is necessary to account for these costs and ensure data remains manageable and valuable.”
Bill Betten, Director of Medtech Solutions, S3 Connected Health
This strategic layer encompasses data acquisition methods, transmission technologies, and the underlying architecture that stores and processes data for future retrieval, analysis, and operational use. When effectively defined and implemented, it provides:
For medical devices — especially smart connected patient devices — connectivity and data transmission are critical. These devices typically rely on IoT. technologies to transmit data, which must be handled reliably with minimal delays. This requires careful management of bandwidth and ensuring seamless connectivity.
Data security is paramount in medical devices, as the data they generate is often sensitive and personal. Protecting patient data through encryption, access control, and compliance with privacy regulations like HIPAA and GDPR is essential to maintaining trust and meeting legal requirements.
Data acquisition involves capturing data from smart connected patient devices, often in real time. Systems must be designed to ensure that data is collected securely, accurately, and with minimal latency.
Once data is collected, it must be ingested into centralized storage systems, often in real time, where it can be integrated with other relevant data sources such as EHRs. This integration creates a comprehensive view of patient health and device performance.
The data architecture design is essential for efficiently storing, retrieving, and managing the data generated by medical devices. The architecture must accommodate various data formats — both structured and unstructured — and ensure the accessibility and security of the data.
Once data is acquired, it must be processed and transformed into a usable format. This involves cleansing the data, normalizing different formats, and ensuring consistency across all sources.
Medical devices often need to communicate with other systems, such as EHRs, which requires interoperability between devices and systems. Ensuring that data can flow seamlessly between different systems often requires adherence to protocols such as HL7 or FHIR.
Managing how data is stored, backed up, and recovered in the event of failure is crucial for medical device companies. Loss of data could severely impact patient care or lead to regulatory compliance issues.
Medical devices generate massive amounts of data in real time, posing significant challenges in terms of storage, bandwidth management, and processing. Without scalable architecture, these data streams could overwhelm systems, leading to delays or data loss.
The functionality or constraints of smart connected patient devices will also influence data strategy, as failing to align the two can result in inefficiencies and missed opportunities. Many companies falter because they overlook the need to tailor their data strategies according to the device's capabilities and limitations.
Data retrieval, data volume, data value, and data frequency are core components that should be carefully considered. If these elements are ignored, data collection efforts may yield information that isn’t valuable or is too difficult to retrieve and use effectively. The focus should always be on ensuring that data collection methods and the device's functional design are aligned.
The technical complexities of managing healthcare data further emphasize the importance of specialized expertise. O'Gorman notes, "collecting data from devices involves complexities with firmware, hardware, and connectivity," which requires careful attention to how data is generated, retrieved, stored, and managed.
These challenges are compounded by the need to differentiate between structured and unstructured data and ensure that the insights generated are valuable. Without skilled professionals to navigate these complexities, medtech companies risk losing the ability to extract meaningful insights from the data they collect.
Although these aspects may seem relevant only to product engineering, collaboration among all stakeholders throughout the development cycle is critical to shaping a viable data acquisition strategy. Data analysts need to work closely with engineers and product developers to understand the practical limitations of the device, including the layers of firmware, hardware, and software that make data collection and transfer possible.
By understanding these constraints, teams can develop strategies focused on capturing meaningful and actionable data rather than overwhelming product design—or, worse, failing to capture the required data. When these groups work in silos, the device’s real-world functionality may not align with data goals, leading to gaps in effectiveness.
"It’s critical to consider functional aspects of data, like battery life, charging the device, and retrieving your data" to avoid complications down the line.”
Aghogho Ekpruke, Research & Development, Medtronic
Betten emphasized several essential and practical considerations for the successful development of data-transmitting smart connected patient devices. The device environment is crucial, as devices need to operate reliably across varied settings, from clinical spaces to home environments, often alongside other equipment.
Clear data requirements help focus on relevant information, preventing unnecessary complexity. Battery life is vital for extended use, requiring careful energy management to minimize recharging needs. Connectivity shapes device functionality by enabling reliable data transmission while robust security safeguards sensitive patient data. User experience also plays a key role; devices must be comfortable and intuitive for sustained use and should limit user actions for data exchange. Finally, efficient data transmission balances power use with timely updates, ensuring devices remain practical and effective. Together, these factors ensure smart connected patient devices meet technical, user, and regulatory needs.
Check out our recent whitepaper for more information on building an effective data strategy framework for connected medical devices. Featuring insights from industry leaders at Cochlear, Dexcom, Medtronic, Omron, Vocxi Health, and S3 Connected Health, the paper offers a practical framework for leveraging data to stay competitive and succeed in a data-driven healthcare landscape. You can read other extracts in this series to understand each layer better: